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Clustering Gene Expression Data using a Split-Merge-BirthProcedureErlandson Ferreira SaraivaDepartamento de Estatística, Universidade Federal de São CarlosLuís A. MilanDepartamento de Estatística, Universidade Federal de São CarlosThe DNA arrays technology has become an important tool for genomic research due its capacity ofmeasuring simultaneously the expression levels of a great number of genes or fragments of genes in differentexperimental conditions. An important point in gene expression data analysis is the identification ofclusters of genes which present similar expression levels since it may help biologists to identify possiblerelationships among genes. We propose a new procedure for estimating the mixture model for clusteringof gene expression data. The proposed method is a posterior split-merge-birth MCMC procedure whichdoes not require the specification of the number of components. The split-merge movements are proposeddirectly in the configuration of the latent variables and are accepted according to the Metropolis-Hastingsprobability. These movements allows a major change in configuration of latent variables in a single iterationof the algorithm, avoiding possible local modes. The birth movement is obtained from the updateprocedure of the latent variables and occurs whenever an observation determines a new cluster. Theperformance of the method is verified using two syntectic data sets and a real data set.141